code: def __init__(
      self,
      root: Union[str, Path],
      folder_in_archive: str = FOLDER_IN_ARCHIVE,
      language: str = "en",
      subset: str = "train",
  ):
      root = Path(root)
      data_dir = root / folder_in_archive / "data" / subset
      wav_dir = data_dir / "wav"
      yaml_path = data_dir / "txt" / f"{subset}.yaml"
      trans_path = data_dir / "txt" / f"{subset}.{language}"
      with open(yaml_path, "r") as stream:
          file_list = yaml.safe_load(stream)
      with open(trans_path, "r") as f:
          self.trans_list = f.readlines()
      assert len(file_list) == len(self.trans_list)
      self.idx_target_lengths = []
      self.wav_list = []
      for idx, item in enumerate(file_list):
          offset = int(item["offset"] * SAMPLE_RATE)
          duration = int(item["duration"] * SAMPLE_RATE)
          self.idx_target_lengths.append((idx, item["duration"]))
          file_path = wav_dir / item["wav"]
          self.wav_list.append((file_path, offset, duration))
  
file_path: audio/examples/asr/emformer_rnnt/mustc/dataset.py
imports: from pathlib import Path
  from typing import Union
  import torch
  import torchaudio
  import yaml
label_desc: Use image node_id 1 to create a new MUSTC object from inherited base classes: torch.utils.data with example: obj = MUSTC(root, folder_in_archive, language, subset)